Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies. |
| Approach: | They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context. |
| Outcome: | The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead. |
Similar Papers
Discourse-Centric Evaluation of Document-level Machine Translation with a New Densely Annotated Parallel Corpus of Novels (2023.acl-long)
Copied to clipboard
| Challenge: | Several recent papers claim to have achieved human parity at sentence-level machine translation. |
| Approach: | They propose to use a dataset with rich discourse annotations to evaluate MT performance . they find that MT outputs differ fundamentally from human translations in terms of latent discourse structures. |
| Outcome: | The proposed dataset builds upon the large-scale parallel corpus BWB . it covers 15,095 entity mentions in both languages and compares them to human translations . |
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing Document-level machine translation systems struggle to handle discourse-level phenomena such as pronoun resolution, lexical cohesion, and ellipsis. |
| Approach: | They propose a graph-based document-level machine translation framework that leverages Large Language Models to model translation flow and discourse structure. |
| Outcome: | The proposed framework outperforms commercial and closed systems in eight languages and six domains. |
Document Graph for Neural Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences. |
| Approach: | They propose to represent a document as a graph that connects relevant contexts regardless of distances. |
| Outcome: | Experiments on IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian show that using document graphs can significantly improve translation quality. |
When Does Translation Require Context? A Data-driven, Multilingual Exploration (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies in context-aware MT attempt to target a small set of discourse phenomena during evaluation, however not in a fully systematic way. |
| Approach: | They develop a multilingual discourse-aware benchmark to evaluate model performance on discourse phenomena in a given dataset. |
| Outcome: | The proposed model improves on previously studied phenomena while uncovering others which were not addressed. |
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (2024.emnlp-main)
Copied to clipboard
| Challenge: | XC-Translate is a large-scale, manually-created benchmark for machine translation . current systems struggle to translate texts containing entity names, but KG-MT outperforms state-of-the-art approaches . |
| Approach: | They propose a method to integrate multilingual knowledge into a neural machine translation model . XC-Translate is the first large-scale, manually-created benchmark for machine translation . they propose KG-MT to integrate cultural-related references into MT models . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by a large margin compared to NLLB-200 and GPT-4 . the proposed method is based on a multilingual knowledge graph and dense retrieval mechanism . |
Document-Level Machine Translation with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks. |
| Approach: | They examine the impact of different prompts on document-level translation quality and discourse phenomena using figures and lines, which are invisible to GPT-4. |
| Outcome: | The proposed models outperform commercial MT systems and advanced document-level MT methods on a number of benchmarks and show potential to become a new paradigm for document- level translation. |
Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models excel at machine translation, but the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. |
| Approach: | They examine how different forms of context influence standard MT metrics and specific discourse phenomena such as formality, pronoun selection, and lexical cohesion. |
| Outcome: | Evaluating multiple LLMs across multiple domains and language pairs, the findings consistently show that context boosts translation and discourse-specific performance. |
Document-Level Machine Translation with Large-Scale Public Parallel Corpora (2024.acl-long)
Copied to clipboard
| Challenge: | Document-level machine translation has inherent advantages over sentence-level translation due to additional information available to a model from document context. |
| Approach: | They propose to use document context to train context-aware models on these datasets and to use it to model document-level phenomena. |
| Outcome: | The proposed models improve translation quality and target document-level phenomena by incorporating contextual information from several preceding sentences. |
Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to managing non-linear dialogue flow are misaligned with the intrinsically hierarchical and branching structure of natural discourse. |
| Approach: | They propose a framework that models multi-turn dialogue history as a dynamic tree structure. |
| Outcome: | The proposed framework enhances task completion rates and improves token efficiency across various LLMs. |
Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)
Copied to clipboard
Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu
| Challenge: | Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. |
| Approach: | They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains . |
| Outcome: | The proposed model performs better in long-text translation, and its performance diminishes as document size increases. |